Introduction

Environmental managers face numerous priority needs,ranging from the protection of critical habitat and mitigating effects of environmental stressors on wildlife to balancing the needs of our natural systems with the impacts of an ever expanding anthropogenic footprint.Yet these challenges are linked by a very simple limiting factor: the need for reliable information regarding when and how the species we seek to manage and preserve are distributed across the landscape (Franklin and Miller 2010). While scientists have long been attempting to understand the relationships between wildlife populations and their spatio-temporal environment, modern technology has allowed for rapid advancements in using modeling approaches to quantify and predict animal space use across time. Assessments based on species distribution maps to identify areas of importance during a species’ main life cycle stages have been used globally and regionally (e.g. Williamson et al. 2013). Though the rapidly expandingfield of species distribution modeling(SDM) offers new approaches for improved model development including Bayesian statistics, maximum entropy,artificial neural networks, genetic algorithms, and other machine learning techniques, most approaches require robust datasets for model development (Segurado and Araujo 2004; Franklin and Miller 2010; Guillera-Arroita et al. 2015). Fine-grained distribution data may be locally available for some species; however, data are rarely available across large extents due to high costs of production.Some regions such as North America and parts of Europe have long-term monitoring efforts for target species from which consistent, quality data can be extracted (Root 1988; Sauer et al. 2003); whereas other, often developing regions, rarely have broad-scale programs despite having rich biological resources (Grenyer et al. 2006; Martin et al. 2012). In some cases, a lack of suffcient data may preempt the use of more advanced methods to address urgent societal needs, and here a balance of using the best approaches possible with available data and resources with clear reporting of shortcomings is warranted.

目前，关于脱除油浆中催化剂粉末物的方法主要有：自然沉降法、过滤分离法、静电分离法、离心分离法、化学沉降法等。

The frequent mismatch between the need for information regarding a species’ spatio-temporal distribution and the data needed to make informed decisions is exemplifi ed by avian infuenza response efforts and wider conservation action in Asia. Wild waterfowl and shorebirds(orders Anseriformes and Charadriiformes) are known reservoirs for low-pathogenic forms of avian infuenza viruses (LPAIV), which have the potential to mutate into lethal forms following entry into domestic poultry populations (Alexander 2007). Outbreaks of highly pathogenic avian infuenza viruses (HPAIV) such as the Asian strain of H5N1 (Xu et al. 1999) have caused considerable damage to the health and economy of more than 60 countries from Asia to Africa since emergence in 1996 (OIE 2017),and the loss of thousands of waterfowl (Liu et al. 2005;OIE 2017). Waterfowl in the family Anatidae (ducks,geese, and swans; hereafter waterfowl) are of particular importance due to their migratory behavior, high abundance, propensity to congregate in high densities, and increased exposure to farmed ducks which can act as silent reservoirs of HPAI (Muzaffar et al. 2010). Due to the risk to both human and wildlife populations, appropriate understanding of the role wild birds’ play in the epidemiology of these viruses is critical. Concurrent with the risk of avian infuenza outbreaks leading to wild bird mortalities, the waterfowl of China face significant population-wide challenges from climate and land use change (Yu et al. 2017). Unfortunately, few studies have incorporated wild birds into geographically explicit models (Gilbert and Pfeiffer 2012), largely because obtaining spatial inputs for these populations is diffcult. Datasets within Asia have been driven by range maps (Williamson et al. 2013) or limited to small regions or few species(Zeng et al. 2015; Dai et al. 2016; Dronova et al. 2016).Therefore, a technique that makes the most of available data to address pressing conservation and human health concerns is of the utmost importance.

The objective of this project was to develop 1 km resolution (1 km × 1 km) binary grid maps of habitat suitability for all species of waterfowl known to spend the winter or breed in China. These base models would not only serve as inputs for modeling transmission risk of circulating avian infuenza viruses at the poultry–waterfowl interface (Prosser et al. 2013), but also provide managers with data relevant to an array of conservation needs. As comprehensive nationwide waterfowl survey data were not available to apply newer techniques of SDM, we took the initial step of mapping potential distributions (habitat suitability) by linking habitat relationships and environmental predictors in a geographic information system(GIS). We validated the suitability models with available census or telemetry data and created a composite suitability map across all species for the breeding and wintering seasons. Here we present spatially explicit habitat suitability models for China’s 30 breeding and 39 wintering waterfowl species. Although previous works with widely different methodologies have created SDM’s for a few targeted species within this region (Moriguchi et al.2013; Zeng et al. 2015; Dai et al. 2016), our models represent thefirst comprehensive set of models spanning the entirety of China. We hope that the assessment provided here will stimulate efforts for other priority areas faced with similar data challenges, and demonstrate that datasets built for a specific objective can have utility for a wide array of conservation oriented issues, particularly when the approach and assumptions are made clear.

Methods

Waterfowl data

Breeding and wintering models as well as locations used for model validation are provided as an example in Fig. 2 for the Bar-headed Goose, a focal species due to its importance to HPAI transmission (Prosser et al.2011) and its decreasing population (IUCN 2017). Suitable breeding habitat for the Bar-headed Goose included the high-elevation plateau of western China and a small section of Inner Mongolia in northeastern China. The pattern of suitable habitat for the breeding season was generally less dense (fewer 1 km cells within a given area)than for the wintering range, for the Bar-headed Goose(Fig. 2) as well as many of the waterfowl species (Fig. 3,Additionalfile 3: Fig. S1). Across species, there was variation in the extent and patchiness of suitable wintering habitat within each species’ range. For example, the Greylag Goose (Anser anser) and Ruddy Shelduck (Tadorna ferruginea) models demonstrate a high density of suitable wintering habitat extending across much of their seasonal range, while suitable wintering habitat for the Common Teal (Anas crecca) was sparser and more patchy across a similar region (Fig. 3). In contrast, the winter model for the Cotton Pygmy-goose (Nettapus coromandelianus)demonstrated a much more confined range of suitable wintering habitat, though it was comparable in patchiness to the Common Teal. Wintering and breeding maps for all species are included in Additionalfile 3: Fig. S1.

Of the 51 waterfowl species listed in MacKinnon and Phillipps (2000) 42 were reported as utilizing China within either their breeding or wintering ranges. Thus,we conducted a review of the English and Chinese literature for China’s 42 waterfowl species (Table 1) following the taxonomy provided by MacKinnon and Phillipps(2000). References included peer-reviewed journal articles, technical reports, as well as unpublished surveys from nature reserves, non-governmental organizations,etc. This literature review spanned several decades, with thefirst included text published in 1985. The database

柴松岩的枕边永远放着录音机和一摞相声磁带，听相声的习惯保持了几十年。她每每入睡前听上一小段，伴着笑声，渐入梦乡，感受明天的美好。这是她保持快乐情绪的一大法宝。

was then structured in three parts: (a) records outlining seasonal habitat requirements for individual species, (b)population and survey counts, and (c) habitat relationship matrices that we developed to relate habitat requirements to land cover predictors (see Model Development below). The database holds 9250 records drawn from more than 1000 references (China Anatidae Network 2012).

With permission from Wetlands International, we used the Asian Waterbird Census (AWC; Li et al. 2009; Wetlands International 2017) to validate the wintering waterfowl models. The AWC provides waterbird survey data collected at wintering sites throughout Asia during January of each year, making it ideal as a source of validation data for this time period. However, a similar consistent source of nationwide survey location data was not available for the spring and summer months to test the breeding models except for one focal species, the Bar-headed Goose (Anser indicus), for which we used satellite telemetry data from a related study (Prosser 2011).

Environmental variables

Remotely-sensed land cover data are readily available across large geographic extents and have been used successfully in modeling species distributions (Gottschalk et al. 2005). Land cover variables (e.g., rice-paddy, lake,river, marsh, grassland, forest, etc.; Additionalfile 1:Table S1) used in this study were derived from 30 m Landsat imagery and distributed by the Chinese Academy of Sciences (CAS) at 1 km spatial resolution (Liu et al. 2002). The land cover dataset is continuousfields,whereby each class is represented as the percent cover within the 30 m2 pixel (e.g. 10% marsh, 64% forest, etc.,summing to 100). We tested variables for correlation to avoid issues of multicollinearity (Graham 2003). Significant correlations were not observed (all below 0.67),however we reduced the data set from 25 variables to 18 (Additionalfile 1: Table S1) based on an a priori list of relevant cover classes. While climatic variables were available through platforms such as the Moderate Resolution Imaging Spectroradiometer (MODIS) program facilitated by the National Aeronautics and Space Administration (NASA), we decided against their inclusion as the literature did not provide adequate depictions of how they would infuence habitat suitability.

Model development and validation

Using a habitat suitability approach (Fig. 1), we created presence-absence predictions for each of China’s waterfowl species (Table 1). Development of habitat matrices included the extensive literature review (see above) and communication with local experts. Habitat relationships were developed by summarizing breeding or wintering habitats from the literature and linking them to appropriate land cover classifications of remotely-sensed Landsat imagery. For example, a summary of the literature indicated that the Bar-headed Goose breeds in habitats including shallow lakes, marshes, lake shores, highland moors, and salt lakes. We translated this summary into an equation that directly links land cover variables: marsh[Landsat category 64], rivers and irrigation channels [41],lakes [42], reservoir or pond [43], and river or lake shore[46] in combination greater than 0%. This descriptive equation was translated into the following equation:

图像识别技术是以提取拉铆销位置周围图像的主要特征为基础，并将待识别图像与图像库的有限特征参数进行匹配，从而达到铆接位置识别的目的。计算机图像识别分为离线训练和在线识别两个步骤，每个步骤分别设计了相应的应用程序。程序核心是BP(back propagation)神经网络算法。其中图像特征提取及离线训练方法直接决定了图像识别的准确性。铆接位置识别程序系统框图如图2所示。

A complete list of species equations can be found in Additionalfile 2: Individual species models. The habitat equations were implemented in a geographic information system using Python coding (Python Software Foundation, Wilmington, Delaware) and ArcGIS 10.1 (ESRI, Redlands, California). The resulting suitability maps for each species were then masked (Fig. 1)using individual species range boundaries produced by MacKinnon and Phillipps (2000), one of the most comprehensive avianfield references available for China. We used the mask to restrict suitable habitat to areas within the boundaries of known ranges for each species. This accounts for the natural distribution of each species and helps to avoid inclusion of regions that might contain suitable habitat but lie outside the range of a given species. While range maps present the broad distribution of a species, our approach identifies the areas within the species range that contain suitable habitat during the respective season. This approach reduces over-prediction of available space inherent in range maps (Graham and Hijmans 2006) and allows managers to focus only on relevant habitat for species of interest. One artifact of this approach is the appearance of a hard transition between predicted presence and absence cells along the outer boundary of each species range. While a soft transition could have been modeled, we chose to retain the current boundaries so that results are explicit and easy to interpret.

The habitat suitability models were validated using AWC data for the winter season and satellite telemetry data for the breeding season (bar-headed goose only).Model validation included testing for errors of omission–identifying grid cells where the model predicts absence of a species but validation data shows that the species was present (Franklin and Miller 2010). Because coordinates for AWC sites represent the centroid of a larger census area that may range from less than 1 km to more than 10 km, we conducted validations at 3 spatial scales to appropriately refect the possible spatial resolutions: (1)immediate (i.e., confirming model prediction as “suitable”within the 1 km pixel that encompasses the validation point), (2) within 5 km, and (3) within 10 km of known observation location. These values were chosen to refect a progression towards the greatest possible locational error in the data. For the latter two, the test was used to confirm that within the specified distance of the validation point (5, 10 km), at least one pixel was predicted as“suitable” for the given species.

Species richness suitability maps

Cumulative species richness maps (Fig. 4) showed distinct spatial patterns for the breeding versus wintering seasons. Areas suitable for high species richness during the breeding season were centered in the northeast and the high-elevation western regions of China. In contrast, waterfowl richness during the wintering season was likely to be highest across much of the low-elevation southeastern part of China, particularly along the Yangtze River basin. Species richness potentials ranged from 0 to 20 species per grid cell for the breeding season and 0–31 for the wintering season, with similar patterns identified from the genus level maps (Fig. 5). Dalai Lake in the northeastern region of China was particularly likely to be important to both genera during the breeding season and was protected by the Dalai Lake National Nature Reserve, while regions of southeastern China such as the Yangtze River basin were of primary importance during the wintering period and had patchy protection provided by separated protected areas. Parsing out individual genera highlighted differences in important areas between groups. For instance, the Changtang Plateau, well protected by a network of large reserves, had far greater suitability to birds in the genus Anser than for those in Aythya. The percentage of suitable habitat within protected areas varied greatly by species and season, with breeding habitat generally better covered than wintering habitat (Table 1). The Ferruginous Pochard (Aythya nyroca), a near threatened species, demonstrates these trends with 31.9 and 9.7% of suitable breeding and wintering habitat within a protected area, respectively.

和逍遥子（张勇）以及他的年轻团队比起来，我确实有些不一样的东西，比如有创始人的光环，有自己的阐释问题、运营管理的方式方法。但是，他们身上的东西我也没有，比如知识结构的全面性、系统性。

Protected areas

A dataset outlining the protected areas within China was obtained from the Chinese Academy of Sciences and their collaborators as outlined in Xu et al. (2017).This dataset represents the product of an extensive effort to identify the spatial boundaries for all protected areas within China, and contains 2412 terrestrial reserves which cover 15.1% of China’s land surface (Xu et al.2017). In this study we used the tabulate areas tool within ArcGIS to determine the percentage of suitable habitat per species located within protected areas, as determined by our breeding and wintering models. This is an approach similar to that of Zhang et al. (2017), which identified the number of important bird areas known to provide habitat for several waterbird species. Data were also visually examined to determine gaps in coverage at locations where habitat was suitable to multiple species within genera Anser or Aythya.

季羡林教授“偷”时间最为辛苦。他曾长期兼任大学系主任和副校长职务，不得不出席很多会议，这对于一个惜时如金的学者来说是很痛苦的。但他也有自己的办法，除了早起晚睡、加班加点，他还在一些无关紧要的会议期间想自己的学问，为文章打腹稿，列提纲，就利用这偷出来的时间，他写出了好几本脍炙人口的散文集。

Results

Of the 42 waterfowl species reported in China, 39 are listed as winter residents and 30 as breeders (MacKinnon and Phillipps 2000). Based on this information, we produced suitability maps for 30 breeding and 39 wintering species (Table 1). Of these birds, 32 were ranked as species of least concern on the IUCN List of Threatened Species (IUCN 2017), while 6, 2, 1, and 1 were listed as Near threatened, Vulnerable, Endangered, and Critically endangered, respectively.

沟通了上面的意思，我说：“这样的问题，你不用解释，也没办法解释。任何解释都会使问题变得更加麻烦。只要你态度明确毫不含糊地说‘不’，就是对她对你最好的负责任的表现，相信女孩会很快地调整好自己的情绪。当然，如果‘是’，就另当别论了。”

The keyfinding of this work was the seasonal changes in species distribution throughout the annual cycle.While there was variation between species, wintering distributions were predominately concentrated in the warmer and lower elevation regions of the southeast while the breeding distributions were more evenly spaced across the northern latitudes and higher elevation regions of western China. These dynamic seasonal distributions have wide reaching implications for waterfowl management. For instance, the areas of greatest risk for interaction between poultry and wild birds for potential zoonotic disease transmission are likely to change throughout the year, and any attempts to limit such interactions must be responsive to these changes(Prosser et al. 2013). Therefore, future projects focused on modeling disease risk should incorporate the spatiotemporal behavior and abundance of wild birds, a step our approach makes possible even in regions of limited data availability. Seasonal distributions also indicate that any successful conservation attempts should include strategies to protect the birds throughout the year, which requires attention to both wintering and breeding habitats (Cui et al. 2014; Dai et al. 2016).

While species level outputs can provide important information, they can also serve as building blocks for a wide array of potential outputs. In order to depict regions which are suitable to the greatest number of species, and therefore likely to host large densities of waterfowl, we overlaid the model outputs for each species and summed the number of species within each cell for which the habitat was predicted to be suitable (Fig. 1). While an image containing all species gives insight into spatio-temporal trends in waterfowl distribution, disaggregation of the species included can allow examination of areas most important to relevant groupings. As an example, we also constructed species richness maps for birds within the genera Anser and Aythya. These genera were selected as they were well represented in our models and highly relevant to avian infuenza and related conservation concerns. Species richness maps were derived separately for both winter and breeding seasons, with all calculations performed using Python coding (Python Software Foundation, Wilmington, Delaware) and ArcGIS 10.1 (ESRI,Redlands, California).

2015年，医院信息中心以手术室为试点，进行了布草管理系统的研发，通过身份识别与手术信息自动核对的方式，限制没有权限的人员领取手术服。智能发放及回收手术服，优化了更衣区管理流程，为管理者提供全程实时动态跟踪查询、处理信息、系统运行情况的监控管理，让手术室/导管室更衣环节更加井然有序，在提高效率的同时有效控制了人员流动，手术室洁净度管理进一步强化。

Discussion

The goal of this work was to develop high resolution spatial distribution maps for waterfowl species across seasons, providing a critical model input that would pave the way for informed management in a data-restricted region. Although there was a scientific deficit for producing data-driven models using newer SDM techniques(Guisan et al. 2013), the societal need to understand where these birds might occur both individually and cumulatively across species remained. Using the best data available, we took a conventional approach of mapping habitat suitability and combined it with model control measures (range masking) to develop and validate habitat models for each species and combine these into species richness data layers. Such data layers, or any of the species specific layers, can now be directly incorporated as inputs into disease models (Prosser et al. 2016),critical habitat identification modeling, or any other need that requires estimation of a species most probable use of space, thereby resolving the challenge of incorporating information concerning wild birds into such efforts.

For the wintering season, the literature indicated that bar-headed geese use natural wetlands, agriculturalfields, riverine wetlands, lacustrine wetlands, and freshwater lakes. The associated equation for land cover included: marsh [64], paddy [11], rainfed [12], rivers and irrigation channels [41], lakes [42], reservoir or pond [43]and river or lakeshore [46] in combination greater than 0:

A total of 406 validation points from the winter Asian Waterbird Census were used to calculate omission rates for 14 of the 37 wintering species (23 species had no validation data). The number of validation points averaged 26 per species with a range of 1–166 (Table 2). Omission rates from validation procedures indicated a strong ability for the models to predict areas where a species might be found (zero errors of omission for 14 wintering species and three spatial scales; Table 2). For the Bar-headed Goose, 13 breeding and 21 wintering locations from a related satellite telemetry project (Prosser et al. 2011)were also used to validate the breeding and wintering models. Bar-headed Goose models had zero omission errors for the breeding season and an error rate of 0.095 for the wintering season (9.5%, or 2 of 21 breeding validation locations occurred within model cells that predicted species absence).

While the spatio-temporal trends in avian infuenza risk we observed have significant implications for how this disease should be modeled, they also have direct ecological implications. For instance, the Tufted Duck(Aythya fuligula) has been found to be especially prone to infection from highly pathogenic avian infuenza, with infections often resulting in mortality (Keawcharoen et al. 2008). While the Tufted Duck is considered a species of least concern, other closely related species within the genus Aythya range from Vulnerable to Critically endangered (IUCN 2017, Table 1). Given the proclivity of some birds within the Aythya genus to suffer mortality from HPAI infections (Keawcharoen et al. 2008; Spackman et al. 2017) it is of great conservation importanceto understand the risks such species face over time and space. Ourfindings suggest that the risk of these species to avian infuenza virus is far from static, changing predictably based upon seasonal behavioral patterns.Informing our understanding of when and where vulnerable species are at greatest risk of exposure not only opens the door for more directed sampling efforts, but also enables the initiation of protective measures that could limit potential population losses. For instance,restrictions could be placed on poultry grazing and transportation through regions of seasonal importance so as to limit potential contact. Such practices could refect the preventative measures applied in countries such as the United Kingdom (Gibbens 2017) and Thailand(Aengwanich et al. 2014) where risk to avian infuenza is determined by geographic region and facility size, and biosecurity protocol are implemented accordingly.

The spatio-temporal changes in species richness observed between our wintering and breeding season models serve to highlight another key aspect of our approach: identifying areas of importance from a habitat conservation perspective. Though already established in the literature, our cumulative species richness maps confi rm the importance of the Yangtze River (Cao et al. 2008,2010; Cong et al. 2011; Williamson et al. 2013) and northeastern China (Williamson et al. 2013) in a spatial context during the wintering and breeding seasons, respectively.Additionally, our genera-specific models allow for the identification of important locations at a muchfiner scale, such as identifying the Changtang Plateau in Tibet as being important to birds of the genus Anser during the breeding season (Zhang et al. 2015a, b). Our models also compared well to species distribution models prepared for this region. For instance, while there were some local areas of disagreement between our models and those produced by Zeng et al. (2015), both identified the Yangtze River Basin as the region within China of the greatest probability of occurrence for the Endangered Scaly-sided Merganser. Similarly, the models produced by Dai et al.(2016) report that key Bar-headed Goose summering habitat is found at Qinghai Lake and the Tibetan Plateau,fi ndings which concur with our results. An oft noted benefit of SDM’s is their usefulness in highlighting crucial habitats to target for conservation and intensive surveys (Moriguchi et al. 2013; Ochoa-Quintero et al. 2010).However, since all that is required to conduct our habitat suitability analysis is knowledge of a species’ habitat needs and publicly available habitat data, this method shows great promise to aid the conservation of species for which critical habitats may be less well understood,especially in these low data scenarios.

The ability of our approach to identify important habitats for waterfowl conservation is demonstrated when our model outputs are overlaid with the spatial boundaries of China’s protected areas. For instance, our outputs identify areas of suitable habitat for multiple species around Taihu Lake, near the mouth of the Yangtze River,where important wintering habitat is only sparsely protected. Our models also identify the need to focus on the protection of wintering habitats which are less protected than the breeding habitats (Cui et al. 2014). Both of these overarching trends are seen with the Ferruginous Pochard, a Near Threatened species. In China, this species breeds mostly throughout the Tibetan Plateau and the northwestern plain, with 31.9% of its suitable breeding habitat covered within expansive protected areas in this region. Conversely, the Ferruginous Pochard winters in southeastern China with only 9.7% of its suitable wintering habitat protected via a patchwork of smaller protected areas. Therefore, this species would likely most benefit from increasing protected area connectivity within its suitable wintering habitat. The information provided by our models is especially important in the context of our study, as several of the species for which we created suitability maps are recognized as species of conservation concern by the IUCN (2017), and recent literature has highlighted the important role of protected areas in species conservation (Zhang et al. 2015a, b)within regions with effective governance (Amano et al.2017), while advocating for the addition of new protected areas within China (Cao et al. 2010; Xia et al. 2016; Xu et al. 2017). Similarly, the outputs of our model can be used to monitor changes in available habitat over time.For instance, by utilizing imagery from different years,researchers and managers can monitor changes in habitat availability for species of interest. The ability of conservationists to identify and respond to such changes is becoming increasingly important in the face of global climate change (Gillson et al. 2013; Yu et al. 2017), and models such as those created in this work provide the opportunity for responsive management even in these low data environments.

Though the comparability of ourfindings to other previous works provides some confidence in this approach,our formal validation provides critical additional support. The validation process demonstrated capacity to predict presence locations for individual species, however, because the validation data included presence-only records, we were not able to assess how well the models predicted the absence of a species, a common issue related to using presence-only data (Hernandez et al.2006). Distribution models tend to be more successful in predicting presence locations than absence locations except for species with a very narrow niche (Brotons et al.2004; Hernandez et al. 2006). We expected our models to be better at predicting presence than absence locations and recognize that this bias could translate into potential over-prediction of a species’ distribution. However,with the intent of this model being to create an input layer for disease interface modeling we felt that a more inclusive approach was appropriate as less inclusive models would run the risk of excluding potentially important wild bird-domestic poultry interfaces. This inclusivity also has benefits when utilized in a conservation context.For instance, identifying the fundamental niche may be best suited when conserving areas around presently atrisk species as this provides buffers around the current areas of use and protects valuable habitat as populations rebound and expand. Yet it is still important to recognize that our approach identifies only suitable habitat for the given species, and thus depicts much less area than species range maps. It is through this enhanced degree of selectivity that important areas can be identified and targeted by managers and researchers as demonstrated above. In any modeling exercise, the implicit limitations should be identified and the model complexity should match the objectives of the study (Merow et al. 2014), as we did here.

One aspect of our approach that warrants special attention from those who wish to apply this method in future work is the impact the selected range map can have on thefinal model output. The benefit of masking model outputs to species range maps is that suitable habitat only within the species known distribution is considered. This provides obvious advantages for the utility of generated products. However, range maps often represent space use trends over many years, and may not refect current distributions. For instance, there have been recent reports of Mute Swan (Cygnus olor) wintering along the Yellow River delta (Brazil 2009), although the behavior is believed to be rare and consist of relatively few individuals. Unfortunately, observations of this behavior occurred after the publication of the range maps, chosen for their coverage of all relevant species,and as such no winter model was developed for this species. Given the rarity of this behavior, it is unlikely such an omission would have significant implications on the utility of model outputs, especially from a disease interface standpoint. Similarly, range maps make all areas of a species range appear equal in use, which can results in misleading results. For instance, White-winged Scoter(Melanitta fusca) winter predominantly along the eastern coast of China but have been known to utilize some inland habitats near Nanchong (MacKinnon and Phillipps 2000). Therefore, while inland habitat use is very limited, our model output does not differentiate the likelihood of use between these two areas of suitable habitat.This issue could likely only be resolved via more complex modeling methods which would not be supported by the data available for this region. While these limitations do not remove the benefit of masking our model outputs with range maps, researchers should carefully consider the implications of potential shortcomings, and test for errors of omission whenever datasets are suffcient.

Our need for the information gained via modeling approaches, without having access to suffcientfine scale standardized datasets, forced us to use a habitat suitability approach. However, science is always changing, and as more data is collected additional analysis options become available. Therefore, we also outlined a framework for improving our current models as new data become available through the expansion and enhancement of waterfowl monitoring during the breeding season as well as the winter via the Asian Waterbird Census and other monitoring efforts (Fig. 1b). Improvements can be made iteratively based on availability of data. For example, an intermediate level model that revises species ranges and adds points for validation could be implemented with minimal additional effort and cost, and as more comprehensive data become available newer approaches to SDM can be employed (Fig. 1b). This multi-level approach,where models are meant to be improved as new information becomes available follows principles of Bayesian logic (Jewell et al. 2009) and Adaptive Resource Management (Allan and Stankey 2009), and can be applied towards a diverse range of distribution needs. Even if this method is not ideal when compared to approaches utilized under conditions of high data availability, it does not change for science-based management. Therefore, we hope that researchers from a wide array of disciplines will recognize the opportunities this approach holds for conducting research and informing management in low data environments.

Conclusions

In this paper we present thefirst nationwide distribution models and predicted species richness maps for many of China’s breeding and wintering waterfowl. We then use the information gained from these models to demonstrate their utility to conservation as it relates to avian infuenza and the identification of critical habitats. We hope this example will encourage similar efforts in other regions with limited data but important needs for understanding the distribution of species across the landscape.We also hope this work will stimulate coordinated efforts to increase the level of input data and overall accuracy of these models. At this current stage, our high-resolution spatial models provide a unique and valuable resource to the research and planning communities across many disciplines from wildlife and habitat management to conservation medicine and beyond.

Availability of data and materials

The authors declare that they have no competing interests.

Competing interests

Data regarding waterfowl telemetry locations used during the current study are available from the corresponding author on reasonable request. Data regarding protected areas are available from Xu et al. (2017) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Xu et al.(2017). Final model outputs are available from the corresponding author on reasonable request.

堤顶高程10.7～12.8m，平均11m。堤身分为两段，第1段孝义河左堤与任高公路交叉处～同口，堤高4～5.5m，堤身土质均匀，主要为轻～中粉质壤土；第2段为韩村～安新大桥堤段，堤高4～6m，堤身土质以轻～中粉质壤土为主，关城分洪口及关城排闸附近表层揭露粉砂和粉土。

Consent for publication

Not applicable.

Ethics approval and consent to participate

All telemetry data reported in this study were collected in accordance with protocol approved by the Patuxent Wildlife Research Center Animal Care and Use Committee.

Funding

以国家图书馆博士学位论文为例：国家图书馆是全国博士论文法定送交单位，根据学位论文管理条例，凡博士论文授予学位后都应送交至国家图书馆保存。另外，国家图书馆还是教育部全国博士论文抽检规定的论文提取单位，而该抽检系全国范围内强制性抽检。因此国家图书馆近年来一直致力于提高博士论文缴送率，近几年博士论文缴送比例已达99%，在国内博士论文收缴单位中收缴比例最高。然而来华留学生博士论文缴送比例远低于此比例，缺缴率较高。

This work was supported by the United States Geological Survey (Ecosystems Mission Area), the National Science Foundation Small Grants for Exploratory Research (No. 0713027), and Wetlands International.

Additionalfiles

Additionalfile 1: Table S1. China Land Cover dataset created by the Institute of Geographical Science and Natural Resources Research(IGSNRR), Chinese Academy of Sciences (CAS) from 30 m Landsat TM satellite imagery.

Additionalfile 2. Individual species models.

Additionalfile 3: Fig. S1. Species distribution models for 32 breeding and 37 wintering Anatidae waterfowl of China.

Authors’ contributions

DJP, CD, RME and ECE conceived and designed the study, DJP and JDS analyzed the data, and DJP, JDS and TM wrote the paper. All authors read and approved thefinal manuscript.

区块链能够很好地弥补当前IoT技术在安全领域方面的缺陷，为IoT技术提供底层安全防护，推动其设备、系统和生态朝着更加安全可信的方向发展；而IoT同样能够为区块链技术的升级提供动力，使区块链能够为更多的应用场景提供安全验证与防护，如图1所示。

Author details

从原油的金融属性来说，宏观环境的冷暖对于油价的影响主要作用在需求侧，分别体现在实物和货币需求两个角度。

1 Patuxent Wildlife Research Center, U.S. Geological Survey, Beltsville, MD 20705, USA. 2 University of Maryland, College Park, MD 20742, USA. 3 College of Nature Conservation, Beijing Forestry University, Beijing 100083, China.4 University of Virginia, Charlottesville, VA 22904, USA. 5 Wetlands International,Horapark9, Ede 6717 LZ, The Netherlands. 6 Natural Systems Analysts, Winter Park, FL 32789, USA. 7 Department of Geography and Environmental Systems,University of Maryland Baltimore County, Baltimore, MD 21250, USA.

Acknowledgements

根据调查表中定性的评价，需要进行量化转化，本文选定高斯分布获取随机数据，进出港从备车到靠泊5 h中11个时刻下状态的风险因子量化数据(一次随机)见图2。

The authors thank Dingnan Lee (Beijing Forestry University) and Shane Heath(USGS) for collating Anatidae literature and developing the database; and Lei Cao (Chinese Academy of Sciences) for discussions on next steps for improving the models. We thank Ruth DeFries of Columbia University for her remote sensing and modeling expertise through all stages of this work. We thank Paul Marban (USGS) for formatting the species distribution maps, and Mike Haramis and two anonymous reviewers for improving earlier drafts of this manuscript. We also thank the many volunteers who contributed to the Asian Waterbird Census by conducting wintering waterbird surveys in China.The use of trade, product, orfirm names in this publication is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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